Making got cheap.
Proof didn't.

Software used to start with a plan.

Teams used to start from a spec. The requirements, the design, what "done" meant. The work answered to it. Then AI let us skip straight to the output: say roughly what you want, take back whatever comes.

Fast to write. Hard to trust.

That shortcut is fine for a throwaway prototype. It falls apart the moment the work has to hold up in front of users, a team, or production. The model is guessing from a vibe, and the guesses fail quietly.

Put the proof first.

Praxis takes the rough ask and turns it into a spec you can audit. Then it proves every step against that spec. The maker checks its own work, a pilot re-derives it against the contract, and a watcher checks the pilot. Nothing ships unproven. How anti-vibe coding works →

Everyone says ten times. Ask what they are counting.

Most of the number is typing. Code written per hour, pull requests opened, tickets closed. Those are real gains and they are easy to measure, which is why they are the ones quoted.

They are also the wrong denominator. Work that has to be reviewed twice was not fast. Work that gets rewritten next sprint was not fast. Work that fails in production and takes three people a week to unpick was the most expensive kind of fast there is. Counting output and calling it speed only holds while nobody checks what happened to the output afterwards.

The only work that counts is the work that survives.

So Praxis moves the whole job rather than one stage of it. The ask becomes a spec you can audit. The spec becomes pass or fail checks before a line is written. The build answers to those checks one gated step at a time. The deploy reads its rules from the same spec, and production watches the targets that spec set. The parts nobody counts, which are the discovery, the design, the estimate, the plan, the review and the evidence at the end, are inside the pipeline instead of around it.

And the evidence arrives with the work rather than after it. Nobody assembles an audit trail at the end, because it was written as each step passed.

The industry's fix is to send people.

The biggest names in AI reached the same conclusion in the same year. Model access alone was not landing deployments, so they started embedding engineers with customers to make the work stick. Billions have gone into forward deployed engineering for exactly that reason, and buyers say responsiveness during deployment is the single biggest factor in whether they renew.

Read that as a verdict rather than a trend. The gap between what a model produces and what an organisation can actually rely on is real enough that the industry is paying salaries to close it, one deployment at a time.

Praxis puts that discipline in the pipeline instead of on a plane.

It runs on every step, on every engagement, at the same standard, whether anyone senior is watching that day or not. People are still the best thing you can put on a hard problem. They should not be the mechanism that checks whether the work was done right.

Bring an idea. Leave with a spec you can build.

You don't need a finished brief. Praxis pressure-tests the idea, whether it's worth pursuing, worth reshaping, or better left alone, then turns what's worth building into a spec you can audit. It flows straight into the pipeline, and the same proof that guards a proposal guards your first plan. The founder path →

Three ways in right now.

The first two turn what you bring into the same spec you can audit. The third builds from that spec, with the same three checks on every step.